CSE 555: Sargur Srihari 1

Introduction to Pattern Recognition

Sargur N. Sriharisrihari@cedar.buffalo.edu

Dept. of Computer Science & EngineeringState University of New York at Buffalo

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What is a Pattern?

“A pattern is the opposite of chaos; it is an entity vaguely defined, that could be given a name.”

A pattern is an abstract object, such as a set of measurements describing a physical object.

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Examples of Patterns

Handwritten Characters

Postnet Bar Code

Fingerprint

UPC BarCode

Animal Footprint

Data Trend

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PR Definitions

• Theory, Algorithms, Systems to put Patterns

into Categories

• Classification of Noisy or Complex Data

• Relate Perceived Pattern to Previously

Perceived Patterns

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Example Problem:Handwritten Digit Recognition

• Handcrafted rules will result in large no of rules and exceptions

• Better to have a machine that learns from a large training setWide variability of same numeral

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Role of Machine Learning

• Principled way of building high performance information processing systems

• ML vs PR– ML has origins in Computer Science– PR has origins in Engineering– They are different facets of the same field

• Language Related Technologies– IR, NLP, DAR, ASR– Humans perform them well– Difficult to specify algorithmically

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Machine Learning

• Programming computers to use example data or past experience

• Well-Posed Learning Problems– A computer program is said to learn from

experience E – with respect to class of tasks T and performance

measure P, – if its performance at tasks T, as measured by P,

improves with experience E.

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Classification Process(Decision as opposed to Inference)

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Pattern Recognition Applications

• SENSORY• Vision

– Face/Handwriting/Hand

• Speech – Speaker/Speech

• Touch– Haptics

• Olfaction– Apple Ripe?

• TEXTUAL DATA• Text Categorization• Information Retrieval• Data Mining• Intrusion Detection• Genome Sequence

Matching

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Pattern Recognition Processes• Objects to be classified are sensed by

transducer (camera)• Signals are preprocessed• Features are extracted• Classification is emitted

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Generalization

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Pattern Recognition System

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Design Cycle

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Document Recognition Applications

• Optical Character Recognition(OCR)

• Handwriting Recognition

• Writer Recognition

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Writer Recognition

Preprocessing

Features

Similarity

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Address Interpretation Problem

Pattern recognition tasks– object recognition (address vs non-address)– two-class discrimination (mp vs hw)– few class recognition (digits)– holistic vs analytical (words)– contextual-hmm(zip codes, words)– Many classes, but cataloged (postal directory)

Contextual Information

• Country/State/City• ZIP Code• Street Name• Primary No (Street/PO

Box )• Secondary No (Apt)• Firm/Personal Name

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